Interoperable Composite Data Units for use in Distributed Computing Execution Environments

ABSTRACT

Disclosed implementations provide executable data, such as artificial intelligence models that can be owned, traded, and used in various execution environments. By coupling a model with a strictly defined interface definition, the model can be executed in various execution environments that support the interface. Coupling the model with a non-fungible cryptographic token allows the model and other components to be owned and traded as a unit. The tradeable composite units have utility across multiple supported execution environments, such as video game environments, chat bot environments and financial trading environments. Additionally, the interface allows for the creation of pipelines and systems from multiple complementary composite units that can be used to train other models and composite units.

RELATED APPLICATION DATA

This application is a continuation-in-part of U.S. application Ser. No.18/107,804, filed on Feb. 9, 2023, which is a continuation-in-part ofU.S. application Ser. No. 17/353,898 filed on Jun. 22, 2021, the entiredisclosures of which is incorporated herein by reference.

BACKGROUND

Computing execution environments, such as distributed video gameenvironments, bot networks, and complex financial transactionenvironments have become ubiquitous. In many such environments,participants can be represented as an object with privileges andcharacteristics. For example, in a video game environment, a playeravatar may have specific capabilities, such as speed, agility, andstrength. The capabilities associated with the player avatar can bestored within the execution environment. However, each environment hasits own formats and protocols. Further, these environments must have acentralized, trusted authority that is the keeper of the data.Accordingly, use of the objects must remain isolated to the creatingexecution environment. As such, activity in other environments does not,and cannot, affect the object. Further, the need for a trusted partyprevents implementation of such objects on decentralized computingexecution environments, such as blockchain networks and otherdistributed ledger technology (DLT), or other interoperable non-DLTsystems.

BRIEF SUMMARY

Disclosed implementations provide composite data units representingexecutable data, such as artificial intelligence models that can beowned, traded, and used in various execution environments. Couplingexecutable data with a strictly defined interface allows anybody to useit in any execution environment that supports the interface. Further,coupling the executable data with a non-fungible cryptographic tokenallows the executable data and other components to be owned and tradedas a unit. In one disclosed implementation, the composite data structurerepresenting the executable data is referred to as a “composite unit”herein. In another disclosed implementation, the composite datastructure representing the executable data is referred to as an“enhanced composite unit” herein. The tradeable composite units haveutility across multiple supported execution environments, such as videogame environments, chat bot environments and financial tradingenvironments. Additionally, the interface allows for the creation ofpipelines and systems from multiple complementary composite units.

One disclosed implementation is a composite data structure recorded onnon-transitory computer readable media for providing a computation modelthat can be implemented in multiple execution environments, the datastructure comprising: an execution pointer specifying execution data canbe associated with a computation model, wherein the computation model isconfigured to process data in a predetermined manner; an interfacedefinition module including a pointer to an interface definitionassociated with the computation model; and a database pointer includinga pointer to a database entry associated with the execution pointer.

BRIEF DESCRIPTION OF THE DRAWING

The foregoing summary, as well as the following detailed description ofthe invention, will be better understood when read in conjunction withthe appended drawings. For the purpose of illustrating the invention,there are shown in the drawings' various illustrative embodiments. Itshould be understood, however, that the invention is not limited to theprecise arrangements and instrumentalities shown. In the drawings:

FIG. 1 is a schematic representation of the structure of a compositeunit in accordance with disclosed implementations.

FIG. 2 illustrates and example of an interface definition of a compositeunit.

FIG. 3 is a schematic illustration of linking between composite unitsand one or more input value matrices in accordance with disclosedimplementations.

FIG. 4 is an example of a code snippet of execution data in accordancewith disclosed implementations.

FIG. 5 is a table illustrating an input value matrix in accordance withdisclosed implementations.

FIG. 6 illustrates a 3-dimensional input value matrix in accordance withdisclosed implementations.

FIG. 7 illustrates a toroidal input value matrix in accordance withdisclosed implementations.

FIG. 8 illustrates examples of multiple composite data units packaged asa memory tree in accordance with disclosed implementations.

FIG. 9 a illustrates an example of an image as execution data inaccordance with disclosed implementations.

FIG. 9 b illustrates another example of an image as execution data inaccordance with disclosed implementations.

FIG. 9 c illustrates another example of an image as execution data inaccordance with disclosed implementations.

FIG. 10 is an example of a composite unit, an interface definition,executable data in the form of an AI model and a data pointer inaccordance with disclosed implementations.

FIG. 11 is an example of execution data containing the execution code inaccordance with disclosed implementations.

FIG. 12 is an example of a composite unit where the execution datadoesn't contain execution code and the interpretation of the executiondata is accomplished by the execution environment.

FIG. 13 is an example of two composite units representing avatars to beused in multiple execution environments. In addition, these compositeunits can be combined.

FIG. 14 is an example of a composite unit where the execution datadoesn't contain execution code and the interpretation of the executiondata is accomplished by the execution environment.

FIG. 15 is an example of two composite units representing avatars to beused in multiple execution environments. In addition, these compositeunits can be combined.

DETAILED DESCRIPTION

Certain terminology is used in the following description for convenienceonly and is not limiting. Unless specifically set forth herein, theterms “a,” “an” and “the” are not limited to one element but insteadshould be read as meaning “at least one.” The terminology includes thewords noted above, derivatives thereof and words of similar import.

The composite units in accordance with disclosed implementations providetrade-ability, interoperability and composability of models, such asartificial intelligence models, that can be implemented in variousexecution environments and moved from one execution environment toanother. By coupling an execution data with a strictly defined interfacedefinition, the composite unit allows multiple execution environments toimplement support for the interface and for other execution data to fitinto that interface. The result is the ability to own and tradecomposite units that have utility across multiple supported executionenvironments. Additionally, the interface allows for the creation ofpipelines and systems from multiple complementary composite units.

A composite unit in accordance with disclosed implementations includes 3components:

-   -   Execution Data a specification of executable code implementing        the model (e.g., a content addressed URL where an executable        code AI model is stored and accessible);    -   Interface Definition: a specification of inputs accepted by the        model and outputs of the model (e.g., a content addressed URL        where an interface definition for the relative model is stored        and accessible).    -   Blockchain Reference: A pointer to a Non-Fungible Token (NFT)        corresponding to the model and stored on a decentralized        computing network, such as a blockchain or other distributed        ledger technology.

FIG. 1 illustrates the architecture of a composite unit in accordancewith disclosed implementations. Composite unit 100 includes contentaddressed model 102, interface specification 104, and token pointer 106(which associates the composite unit 100 with an NFT stored ondecentralized ledger 110. Note that elements 102, 104, and 106 are dataelements stored on non-transitory computer-readable media as a datastructure. The elements can be linked in various manners, by beingstored in a single data structure, through relational tables, or thelike. The term “pointer” as used herein, encompasses all mechanisms forlinking/associating multiple specified elements, either directly orindirectly. Also, the elements can store the corresponding data or code,or can otherwise specify the data or code through a URL or otheraddress, a link, or the like. For example, content addressed model 102can include the model code for executing the model or, as illustrated inFIG. 1 , include an address to the storage location of the model code.Decentralized ledger 110 can be part of a decentralized environment suchas a blockchain network. The NFT is a unique token that can be used toidentify and represent ownership of composite unit 100, regardless ofthe executing environment in which composite unit 100 is being used atthe time.

As a simple example of composite unit 100, consider the classic computergame PONG™. An example interface definition for a composite unit for thecomputer game PONG™ is shown in FIG. 2 . As illustrated in FIG. 2 , theinput, from the most recent N (N=2 in this example) frames of game playare shown at 202 and 204. The inputs include x and y positions of theball, and each player's paddle, from which the following can be deduced:

-   -   Ball position, speed, and trajectory,    -   Opponents paddle position, speed, and trajectory; and    -   Player's own paddle position, speed, and trajectory.        The output of the interface in this example is the player's        paddle movement instructions for the next frame, as shown at        206. The x and y values will be added to the current player's        paddle position.

By defining a strict interface for model inputs and outputs the modelscan be deployed across multiple environments that provide support forthat interface. The constraints of standard PONG™ are very simple andthe physics are linear, but this interface could be applied to multiplevariants with different constraints and physics. By tweaking the numberframes taken as input, models could be trained for a game variant withmore complex constraints and non-linear physics. The disclosedimplementations can be used to create a diverse ecosystem of competitivePONG™ tournaments where AI models are trained to compete across thefield in different variants of the game. Disclosed implementations canbe applied to more complex environments in gaming and beyond asdescribed in examples below.

Composite units can be linked across execution environments (referred toas “arena's” herein) via an input value matrix which is a data structurecontaining a set of values that can be mapped to input variables withinthe arena and composite unit. This allows both consistency andflexibility in how the composite units are deployed by providing asingle input reference but giving the arena developers the choice on howthey are mapped. Arenas can refer to any environment the composite unitmight interact with, examples include a level within a video game, anentire game, a trading bot, and/or a single interaction. “Arena agents”are the code that executes the outputs of the composite unit.

FIG. 3 illustrates multiple composite units, one for each arena, coupledto an input value matrix. Each composite unit, 300 a, 300 b, and 300 cin this example, has a corresponding arena agent 302 a, 302 b, and 302c. Input value matrix 304 is coupled to each composite unit through thecorresponding arena agent.

A very simple example input value matrix, in table form, is set forthbelow.

Value 1 5 Value 2 2 Value 3 6

With respect to the video games PONG™ and SPACE INVADERS™, applicationof the input value matrix might be as follows. In PONG™, an arenadeveloper might want to add a constraint to the agent for maximum paddlespeed, this can be achieved by mapping Value 3 in the input value matrixto the speed variable within the controller script, which can be part ofmodel of the corresponding composite unit 100 (see FIG. 1 ). A snippetof an example of model code which controls movement of the paddle isillustrated in FIG. 4 . The speed variable is indicated at 402. In anexample relating to SPACE INVADERS™, an arena developer might want toadd a constraint to the agent for maximum speed of the space craft bymapping Value 3 to the input value for the variable speed. As notedbelow, this mapping can be a source of uniqueness and thus is referredto as “genome mapping” herein.

The input value matrix described with respect to FIG. 3 , and FIG. 4 isa very simple example for illustrative purposes. FIG. 5 illustrates amore complex input value matrix 500 in accordance with disclosedembodiments. At least some of the variables in the input value matrix500 represent attributes of a player entity in a video game. In theexample of FIG. 5 , a set of variables can be related to a category ofattributes. For example, attributes 502 represent strength of the playerentity with respect to the relevant arena, attributes 504 representintelligence of the player entity with respect to the relevant arena,and attributes 506 represent agility of the player entity with respectto the relevant arena. Each set of values in input value matrix can bemapped to an array of variables in model code or each variable can bemapped to an individual variable in the model code. The input valuematrix can be randomly generated and each game developer candesignate/tag areas of the matrix as corresponding to specific skills.Tags can be associated with each area and game developers can leveragethe tags created by previous developers to create a similar skill setfor a similar game. Developers of different types of games may choose tocreate and or use a set of tags that is very different from a set oftags for a different game. Therefore, the input value matrix can be aset of values and associated TAG cloud(s) that can beused/grouped/tagged as desired. The game developer can decide which tagsto use based on the type of game and desired play characteristics, butlikely would want to be consistent with other similar games.

As noted above, the composite unit in accordance with disclosedimplementations can be applied to video games. Disclosed implementationsof a gaming platform and protocol example are described in greaterdetail below. The platform allows users to mint game player entities(referred to simply as “players” below) and upgrade their statistics,earn, buy, and sell them as NFTs within the ecosystem. The model codeincorporates machine learning to create a “brain” that can change andadapt within, and as a result of, gameplay.

The protocol and platform allow multiple games to be created bydevelopers to interact with players. Different players can havedifferent levels of various relevant skills (such as strength, speed,intelligence, . . . ). A player may be an interactive intelligentnon-player character (NPC) in a game with human backed players. An NPCis character or other entity in a game that is not controlled by theperson(s) playing the game. A player can be defined by 3 parts, the base“frame”, a “form” defining the player's aesthetic and attributes (forexample, the frame and form can be in the form of the input value matrixdefined above), and a trainable “brain” (for example in the form of theexecutable model described above. A user can create new players byminting a new NFT Pack including these components with defaultattributes. Some frames, forms and/or brains will contain rareattributes or may start from a higher level in their potential meaning,for example, that the corresponding player is a faster learner who takeless time to reach the pinnacle of certain skills.

To play a game, a frame, form, and brain are selected and linked to oneanother. With gameplay each component will be modified in unique ways.The combination of frame, form and brain impact the learning model andcan produce an extremely large set of decision-making processes andcombinations in the specific machine learning model storage for theplayer (i.e., the brain). Simply attaching a “more advanced” brain or“more capable” form to a frame might not immediately result in asuperior player because a brain that has been trained to use a form withcertain attributes will need to relearn when combined with a form withdifferent attributes or with different values for those attributes. Eventwo frames with forms and brains with identical attributes might alsodevelop totally different training in the brain model based on gameplay. This allows for an enormous universe of unique “personalities” todevelop.

This model of separating attributes and attribute values, which can beattached and detached from a frame, allows users to design the ultimatestrategy by combining unique parts of the player from different frames,forms and brains. Significantly, this structure allows a user to choosea form for a specific arena or a brain for a specific task or strategyor when facing a specific opponent. Each form or brain can becategorized, for example, as 1 of 5 types; Defective, Common, Rare,Epic, and Legendary. The Players can be defined using a .yaml file thatholds the attributes and is stored using IPFS. YAML is a serializationlanguage that is often used as a format for configuration files as areplacement for languages like JSON. IPFS is a well-known peer-to-peerhypermedia protocol. YAML is only one example of a file structure/formatthat could be used to store data defining a player and attributes. As aresult of this configuration, “memories” are immutable, decentralized,and can be linked specifically to an NFT.

A frame includes a set of universal attribute values assigned atminting. The values can start low but could be upgraded by completingtasks in the platform and/or purchasing boosts with the platformcurrency. For example, the frame attribute values can correspond to:

-   -   Strength;    -   Fitness;    -   Speed;    -   Dexterity;    -   Intelligence;    -   Charisma;    -   Perception;    -   Luck; and/or    -   Size        Forms can be game-specific and can have attribute values        providing additional skills or attributes, to modify skills and        attributes, for a specific task, such as a game against a        specific opponent. In order to enter an arena, an arena specific        form can be required to be attached to the frame. This allows a        frame to participate in multiple arenas if it has multiple        forms. Forms can also contain multipliers of frame stats that        might be useful for a specific arena. These can be randomly        assigned and can include rare attributes. Once a form is minted,        games can upgrade skill levels internally through mapping        multipliers or the like.

Brains can be defined as frames, i.e. a memory address associated withan NFT and storing the code for executing a learning model. Brains canbe used with various forms. However, a new form may require the brain tolearn how to use the modified attributes and attribute values specifiedtherein. Brains also have attributes which can boost the attached frameand might be more useful to a specific arena(s). Frames can containmultiple memories which store the training for a specific learning modeland form combination to thereby allow training for several combinationsfor a specific arena.

In order for a brain to learn, it needs to be trained through activity,e.g., game play. Training can be accomplished in a “gym” platform. A“gym”, as used herein, can be a GPU-powered machine learning modeltrainer. The model is influenced by the attributes associated with theplayer NFT. As the learning model of the brain uses a neural network,the specific outcomes of training a specific player are unique. Playersare able to model their attributes by training at a gym, which in turnmakes their AI better at playing the game. Each player can have an .onnxfile that is updated each time they train at the gym. ONNX is an openformat built to represent machine learning models. Training at the gymcan be a process similar to mining cryptocurrency, where GPUs are usedto train the unique brain of your NFT. The protocol can provide anincentive to those who host the gym as the users of the gym can berequired to pay for gym usage. During a gym session a user will be ableto see their player improve, by monitoring the attributed through a fileviewer for example, and be able to end the session once their decidedoutcome is

An Enhanced Composite Unit (ECU) is disclosed below. The ECU expands onthe composite data unit disclosed above to provide additional utility.An ECU is another example of a composite data structure in accordancewith disclosed implementations. With an ECU, the actual execution datafunctions can be accomplished external to the ECU at the executionenvironment level. The execution data may leverage an interpreter or thelike for execution as will be clear from the disclosure below. Further,the execution data can be any computing functionality and is not limitedto ML or AI. The execution model can be represented by data (referred toas “execution data” herein), such as an image, video, or anothermultimedia as opposed to merely executable code. The execution model canbe executable code, although not necessarily exclusively as such. Theexecution data need not be internal to the ECU. For example, the ECU canbe associated with the execution data through a pointer or relatedelements in relational database. The execution data and any related codecan be part of an execution environment that is separate from the ECU.

For a given execution model, there could be several sets of executiondata or execution codes that will execute the same model in a similarway to provide compatibility and optimization for various executionenvironments. The interface definition of the ECU can be similar to thatdescribed above with respect to the composite unit. However, thedatabase reference of an ECU is not limited to a non-fungible token oranother type of token on a decentralized ledger. For example, thedatabase reference can be an entry in more conventional, centralizedstorage or database systems, such as Amazon S3 and the like.

Further, in the ECU, the concept of a “Brain” is expanded upon toinclude an input value matrix to which a memory tree (discussed indetail below) can subsequently be attached. An input value matrix can beany sequence of values/characters (letters, numbers, symbols, etc) thatcould be represented in any shape (sphere, double helix, torus, etc) andany dimensions (2D, 3D, etc.). The input value matrix can be immutable(by being stored on a decentralized ledger for example) in a manneranalogous to human DNA. Accordingly, the input value matrix is sometimesreferred to as a “genome matrix” herein.

Examples of the data structures of the input value matrix of ECUs inaccordance with disclosed implementations are illustrated in FIGS. 6 and7 . While the input value matrix of FIG. 5 (which can be used inconnection with the ECU) is two dimensional, the input value matrix canhave any number of dimensions and can represent various geometricshapes. For example, input value matrix 600 of FIG. 6 is cubic(3-dimensional). One side of input value matrix 600 is shown at 602 andanother side is shown at 604.

As noted above, variables in the input value matrix 600 can representattributes of a player entity in a video game and a set of variables canbe related to a category of attributes. For example, attributes of side602 could represent strength of the player entity with respect to therelevant execution environment, and attributes of side 604 couldrepresent the intelligence of the player entity with respect to therelevant execution environment. Again, each set of values in the inputvalue matrix can be mapped to an array of variables in model code oreach variable can be mapped to an individual variable in the model code.The input value matrix can be randomly generated and each game developercan designate/tag areas of the matrix as corresponding to specificskills, traits, values, attributes, properties, characteristics,limitations, rules, etc. Other than having more than 2 dimensions, inputvalue matrix 700 can be similar to input value matrix 600 and can beapplied in analogous manners. As another example, FIG. 7 shows an inputvalue matrix that is substantially toroidal.

An ECU is sometimes referred to as a “memory herein. Multiple ECUs, (or“memories) can be packaged in a container and associated with oneanother in a tree-like manner through the use of a data structurereferred to as a “memory tree” herein. Memories can allow the ownerthereof to have some interaction in a particular execution environment.For example, one of these execution environments would be the ability togenerate an ECU from another ECU, such as the generation of an imagefrom a collection of other images or a single image (see FIG. 9 a ). Theexecution data of a memory (ECU) could be an AI model, executable code,or even an image. A memory tree is a node tree for memories/ECUs. Memorytrees save memories as discrete entities and providing a “versionhistory” for the end-user to return to and start a “new branch” asdesired. FIG. 8 illustrates two examples, 902 and 904, of memory trees.Each memory tree includes multiple ECUs associated with one another in atreelike arrangement. The term “pointer”, as used herein, can include acollection of multiple pointers, such as a memory tree. Each pointer canspecify a collection of execution data. Each of the execution datacollections can share the same interface definition. Thus each pointeris associated with an interface definition. As an alternative, theentire memory tree, or portions thereof, can share a single interfacedefinition. In such an example, the memory tree structure thus containsa collection of execution data components. Each collection of executiondata components can be a treelike structure that links the executiondata with other execution data from the same composite data structure orECUs with other ECUs.

It is possible to define another structure as a container of memorytrees, where each memory does not contain the interface of a compositeunit, instead each memory tree structure contains the interface thateach memory points to.

Each container can define a Brain, as disclosed above, that is unique inmultiple ways:

-   -   The nonfungibility of an associated NFT    -   The nonfungibility of the associated genome matrix as an input        or layer of interpretation to the execution data    -   The nonfungibility of the associated Memory Tree

The Brain can be augmented over time by adding composite units to thememory tree. In many implementations, composite units will be unique(non-fungible), and as such, a hash can be created to verify theuniqueness. However, some implementations of an ECU can be non-unique(fungible). However, even in this case, the interpretation of the uniqueinput value matrix by the non-unique composite unit will become unique.The interpretation could be hashed to verify the uniqueness. In someimplementations, the ECUs can be semi-fungible. In such a case,uniqueness is dependent on the execution environment. Not all executionenvironments will guarantee the uniqueness of the interpretation of anymatrix by any composite unit but there exists at least one executionenvironment that will guarantee such uniqueness. For example, in anexecution environment that doesn't utilize a diverse enough sample ofthe matrix as their “genome mapping”, two composite units associatedwith two distinct input value matrices may produce the same result.Composite units, including ECUs, can be linked across executionenvironments (also referred to as “arena's” herein) via an input valuematrix (which, as described above, is a data structure containing a setof values that can be mapped to input variables within the arena and thecomposite unit).

Multiple Brains can be associated together, in a process termed“splicing”. You can imagine a situation where an owner may want tocombine two or more Brains or multiple owners may coordinate to combinetheir Brains together. This “splicing” will produce a new Braincontaining its own input value matrix/matrices, which could be aduplicate of one of the original Brains input value matrix or some sortof interpolation of the between them. In the spliced Brains, thememories could be duplicates, transfers, blends, etc. In a simple PONG™example, where a composite unit is a controller of the PONG™ bar, anexecution environment developer may decide to associate part of inputvalue matrix with the speed of the bar displacement.

In other disclosed implementations, composite units can include digitalimages or other multimedia. This allows the creation of unique artstyles (as an ECU or a collection of ECUs. As shown in FIG. 9 a , animage (input 1) 902 can be processed by ECU 910 (which includes an image(input 2) 904 and associated execution data 906). Execution data 906 canbe any type of data structure specifying an execution model as disclosedabove. In this example, execution data 906 can be a matrix defined bythe image data itself, or a portion thereof. Execution data 906 can be awatermark formed on image 904. Any unique data in or associated withimage 904, mapped to execution data or other execution code (defined inan execution environment and not shown in FIG. 9 a ), can be used asexecution data 904. In the example of FIG. 9 a the execution data maycontain both the image and the add/overlay/blend operation, or only theimage while the operation is defined within the execution environment toresult in an output of image 9008.

As noted above, the execution data can take various forms. FIG. 10illustrates pseudocode of execution data 1000 in the form of an AImodel. FIG. 11 illustrates pseudocode of execution data 1100 in the formof an executable code. FIG. 12 illustrates pseudocode of execution data1300 in the form of an image, similar to the example of FIG. 10 .Execution data 13 specifies that an image reader can read the entire480×640 image to generate a matrix and that the matrix is input intoexecution code found at, or linked to, the specified blockchainreference. FIGS. 10 and 11 illustrate examples in which theinterpretation of the execution data is accomplished by the compositeunit, whereas, in the example of FIG. 12 , the interpretation is left tothe execution environment.

Users can connect to a game using their web3 wallet. The user thenselects a player which is a combination of frame, form, brain andmemory. Multiple players can be selected to create a team in the case ofteam play such as football. Two teams are required for a match/game. Thegame starts and the players are loaded, the game checks to ensure thatNFT associated with the combination of frame, form, brain and memory isowned by the user when they enter the arena. The two sides compete toscore enough winning points/goals in a specific time period.

The output of this result can be stored on a distributed ledger, such asa blockchain network against the record of the relevant NFTs. Thisenables an ecosystem of economic incentives and activities to developaround the players and the outcomes of Games. Game, player, and teamstats can be displayed in various manners. Users can view an inventoryof their frames, forms and brains as well as the makeup of their playersand how the stats impact the players.

A user interface can be provided to allow players to be modified byattaching a combinations of frames/forms/brains. Users could wager onthe outcome of games or rent a team to play a match or borrow a playerto upgrade their team.

The platform includes a distributed ledger having a native token to beused for the payments noted herein. The token can be mined using aliquidity mining event. Once a user holds a native token, the user canuse the native token to mine (or buy) a pack of a frame/form/braincombination. Packs will have a random chance of spawning a rarecomponent of each. Mining can be based on a fair distribution curve, anda minimum stake can be required to mine a pack. The time it takes tomine can be reduced based on a user's staked amount. This allows earlyor strong supporters to benefit as well as later or smaller supportersto participate.

To make the game fair even for smaller participants, an increase instake need not necessarily increase your individual chance of getting arare attribute upon minting of an NFT. However, larger stakeholderscould be able to mine more packs in the same amount of time with respectto smaller stakeholders. Packs can be released in editions and, overtime, editions can contain new attributes as the models evolve or someeditions may contain limited runs. However, because the performance of aplayer is determined by its training and experience even a “low” speccharacter has a chance of developing a winning capability/strategy. Thenative token can also be used for the payments between providers of GPUsto gyms and users of those gyms. Native tokens can also be used topurchase players from other users, to buy access to an arena, or topurchase cosmetic items or loot boxes for players.

The specific examples described above relate primarily to video games.However, the disclosed implementations can be applied to variousapplications and the “player” entity could be, for example, a chat bot(to give an individual personality to an online friend), a personalassistant that is truly personal, and/or a trading bot which acts onbehalf of its owner or its community to accomplish transactions inaccordance with a dynamic trading strategy. Any task that could becompleted by an intelligent automated agent could be accomplished usingthe disclosed implementations.

Further, a composite unit can be applied in a metaverse/Virtual Reality(VR)/Augmented Reality (AR) environment. For example, a composite unitis can represent an avatar (representing a character) within multipleexecution environments. The composite unit can represent non-AI aspectsof the avatar, such as an image. Further, the composite, can be morecomplex where and specify the avatar as a 3D asset articulated bydifferent AI models. The composability of composite units describedabove can be applied to a composite unit representing an Avatar can beaugmented with other composite units to add clothing, weapons,characteristics, or the like. For example, one can imagine an scenariowhere a composite unit of a football player avatar could be dropped intoa football game, changing the visual representation of the gamecharacter, The same avatar, dropped into an open world battle game (MMORPG) along with a second composite unit, could compose together tocreate an enhanced avatar representation that could have uniquecharacteristics, traits, etc. per the rules of the executionenvironment. FIG. 13 illustrates an example of two composite units,composite unit 1302 and composite unit 1304, representing avatars to beused in multiple execution environments. In addition, these compositeunits can be combined as shown at 1310.

As noted above, the interface of the composite units allows for thecreation of pipelines and systems from multiple complementary compositeunits. For example, composite units can be used in seriatim and/orparallel to produce pipelines of composite units. Of course, the inputsand outputs of successive sections of the pipeline should substantiallymatch. As a simple example, if the output of a first composite unit inthe pipeline is ball speed and ball direction. The composite unit orunits connected to the output should accept, as input, ball speed andball direction

In the context AI gamification, interacting with a model is critical. AImodels can be defined as composite units that interact with an executionenvironment (a video game for example) to “train” the executionenvironment (e.g., add features, configure operating parameters . . . .In general, AI models can be described as a set of matrices that accepta set of inputs to produce a set of outputs. Rather than seeing theconcept of “training” as a component of the AI model, the very makeup ofthe model itself, disclosed implementations can describe “training” ashaving a function within a particular execution environment, e.g.,generating/modifying a ML/AI model. For example, If a composite dataunit is saved in correspondence to tokens on a blockchain, a marketplacefor semi-fungible, fully interoperable separated training and inferenceenvironments, as well as the composite unit(s) that could create suchenvironments, can be created—allowing people to buy, sell, and trade thecomposite data units in open, decentralized forums.

As noted above, the interface of the composite units allows for thecreation of pipelines and systems from multiple complementary compositeunits. For example, composite units can be used in seriatim and/orparallel to produce pipelines of composite units. Of course, the inputsand outputs of successive sections of the pipeline should substantiallymatch. As a simple example, if the output of a first composite unit inthe pipeline is ball speed and ball direction. The composite unit orunits connected to the output should accept, as input, ball speed andball direction

In the context AI gamification, interacting with a model is critical. AImodels can be defined as composite units that interact with an executionenvironment (a video game for example) to “train” the executionenvironment (e.g., add features, configure operating parameters . . . .In general, AI models can be described as a set of matrices that accepta set of inputs to produce a set of outputs. Rather than seeing theconcept of “training” as a component of the AI model, the very makeup ofthe model itself, disclosed implementations can describe “training” ashaving a function within a particular execution environment, e.g.,generating/modifying a ML/AI model. For example, If a composite dataunit is saved in correspondence to tokens on a blockchain, a marketplacefor semi-fungible, fully interoperable separated training and inferenceenvironments, as well as the composite unit(s) that could create suchenvironments, can be created—allowing people to buy, sell, and trade thecomposite data units in open, decentralized forums.

FIG. 14 illustrates another disclosed implementation in which acomposite data unit is used to provide a computation model that can beapplied in multiple execution environments. As shown in FIG. 14 ,composite data unit 1400, which can be a data structure recorded onnon-transitory computer readable media, includes AI inference model1402, interface definition 1404 and pointer 1406 which points to adatabase reference entry associated with the AI model 1402. For example,AI inference model 1402 can specify predetermined operating parameters,whereby the AI model will cause an execution environment (such as PONG™game 1420) to perform an activity (play of the PONG™ game) based on thepredetermined operating parameters. AI inference model 1402 can beapplied to process observation (input) and return action (Output) of theexecution environment to cause the execution environment to operate in adesired manner. As a simple example, AI model 1402 could add a “spin”feature to the PONG™ game to allow a player to apply spin by moving theracket during impact with the ball to thereby cause the ball to travelalong a non-linear path. Other aspects of composite data unit 1300 canbe similar to the composite data units described above.

In FIG. 14 , AI model 1402 determines how to move the ball given thelocation and/or movement speed/direction of the two paddles. Wheninteracting with the game, a user selects/submits a model. Then,execution environment 1420 (the PONG™ game), will run the game, callingfor the usage of AI model 1402 to describe the movement of one paddleover many iterations. This AI model 1202 is applied to processobservation and return action of execution environment 1420.

FIG. 15 illustrates a disclosed implementation in which a “training”composite data unit is interacts with an execution environment. Asdiscussed in greater detail below, the execution environment can beanalogized to a gym with all the weights and physical fitness machines.The training composite data unit can be analogized to a human coachgiving instructions and monitoring the athletes (AI model) duringtraining. As shown in FIG. 15 , composite data unit 1500, which can be adata structure recorded on non-transitory computer readable media,includes an execution environment 1502 (including an AI model),interface definition 1504 and a pointer 1508 which points to a databasereference entry associated with the trainer X 1512. Training compositedata unit 1502 also includes execution data specifying predeterminedtasks and an order in which the predetermined tasks are performed,whereby the execution data will cause the execution environment todirect model 1506 to perform the predetermined tasks in accordance withthe order within the execution environment operative to modify the AImodel of the execution environment. Again, as an analogy, the compositedata unit can be thought of as a coach, and the execution environment isa gym with all the weights and physical fitness machines. It could alsocontain data for other types of training could happen, like preparingfor a specific run, and the like. The composite data unit will consistof executing or directing the model to perform certain tasks in acertain order within the execution environment and the composite dataunit will also be responsible for modifying the model running in theexecution environment, in accordance with “training” parameters. Theexecution environment can be distinct from the CDU. An executionenvironment suitable for a trainer CDU can be, for example thecombination of a PONG™ compatible environment with a wrapper around itto support training tasks. The trainer X CDU is a component that can beplugged into the wrapper to define the training tasks.

In implementation of FIG. 15 , the pointer to the interface definitioncan specify an execution environment that includes execution dataspecifying tasks and an order in which the specified tasks areperformed, whereby the execution data causes an AI model to perform thespecified tasks in accordance with the order to thereby modify the AImodel according to performance the training composite data unitmeasures.

The process describes in FIG. 15 can be applied one level up to build a“trainer of trainers”, where the trainer of trainers composite unitdata, once plugged in a suitable execution environment will generate atrainer X 1512. Users can connect to a game using their web3 wallet. Theuser then selects a player which is a combination of frame, form, brainand memory. Multiple players can be selected to create a team in thecase of team play such as football. Two teams are required for amatch/game. The game starts and the players are loaded, the game checksto ensure that NFT associated with the combination of frame, form, brainand memory is owned by the user when they enter the arena. The two sidescompete to score enough winning points/goals in a specific time period.

The output of this result can be stored on a distributed ledger, such asa blockchain network against the record of the relevant NFTs. Thisenables an ecosystem of economic incentives and activities to developaround the players and the outcomes of Games. Game, player, and teamstats can be displayed in various manners. Users can view an inventoryof their frames, forms and brains as well as the makeup of their playersand how the stats impact the players. A user interface can be providedto allow players to be modified by attaching a combinations offrames/forms/brains. Users could wager on the outcome of games or rent ateam to play a match or borrow a player to upgrade their team.

The platform includes a distributed ledger having a native token to beused for the payments noted herein. The token can be mined using aliquidity mining event. Once a user holds a native token, the user canuse the native token to mine (or buy) a pack of a frame/form/braincombination. Packs will have a random chance of spawning a rarecomponent of each. Mining can be based on a fair distribution curve, anda minimum stake can be required to mine a pack. The time it takes tomine can be reduced based on a user's staked amount. This allows earlyor strong supporters to benefit as well as later or smaller supportersto participate.

To make the game fair even for smaller participants, an increase instake need not necessarily increase your individual chance of getting arare attribute upon minting of an NFT. However, larger stakeholderscould be able to mine more packs in the same amount of time with respectto smaller stakeholders. Packs can be released in editions and, overtime, editions can contain new attributes as the models evolve or someeditions may contain limited runs. However, because the performance of aplayer is determined by its training and experience even a “low” speccharacter has a chance of developing a winning capability/strategy. Thenative token can also be used for the payments between providers of GPUsto gyms and users of those gyms. Native tokens can also be used topurchase players from other users, to buy access to an arena, or topurchase cosmetic items or loot boxes for players.

Various known technology platforms and protocols can be used inconnection with the disclosed implementations. The NFTs can be mintedusing the ERC-1155 token standard. Such NFTs are specifically for gamingand enable more efficient trade and transfer on the Ethereum networkthan is possible through ERC-721 alternatives.

CHAINLINK™ RNG (Random Number Generator) and VRF (Verified RandomnessFunction) can be used to provide randomness both in game mechanics (suchas a coin toss for where the ball starts) and in minting mechanics forrare attributes associated with the NFTs. The Chainlink oracle networkcan also be used to enable the NFTs to have dynamic attributes whichbuild up during game play. SYLO protocol, an ecosystem made up ofdigital consumer wallet software, applications, infrastructure &developer tools, can be used for in game chat and marketplace chat aswell as for NFT wallets. IPFS can be used for storing the memoriesassociated with an NFT and ensuring the brain in a game is using thecorrect NFT. UNITY can be used for the game engine.

The specific examples described above relate primarily to video games.However, the disclosed implementations can be applied to variousapplications and the “player” entity could be, for example, a chat bot(to give an individual personality to an online friend), a personalassistant that is truly personal, and/or a trading bot which acts onbehalf of its owner or its community to accomplish transactions inaccordance with a dynamic trading strategy. Any task that could becompleted by an intelligent automated agent could be accomplished usingthe disclosed implementations.

Further, a composite unit can be applied in a metaverse/Virtual Reality(VR)/Augmented Reality (AR) environment. For example, a composite unitis can represent an avatar (representing a character) within multipleexecution environments. The composite unit can represent non-AI aspectsof the avatar, such as an image. Further, the composite, can be morecomplex where and specify the avatar as a 3D asset articulated bydifferent AI models. The composability of composite units describedabove can be applied to a composite unit representing an Avatar can beaugmented with other composite units to add clothing, weapons,characteristics, or the like. For example, one can imagine an scenariowhere a composite unit of a football player avatar could be dropped intoa football game, changing the visual representation of the gamecharacter, The same avatar, dropped into an open world battle game (MMORPG) along with a second composite unit, could compose together tocreate an enhanced avatar representation that could have uniquecharacteristics, traits, etc. per the rules of the executionenvironment. FIG. 13 illustrates an example of two composite units,composite unit 1302 and composite unit 1304, representing avatars to beused in multiple execution environments. In addition, these compositeunits can be combined as shown at 1310.

As noted above, the interface of the composite units allows for thecreation of pipelines and systems from multiple complementary compositeunits. For example, composite units can be used in seriatim and/orparallel to produce pipelines of composite units. Of course, the inputsand outputs of successive sections of the pipeline should substantiallymatch. As a simple example, if the output of a first composite unit inthe pipeline is ball speed and ball direction. The composite unit orunits connected to the output should accept, as input, ball speed andball direction.

It will be appreciated by those skilled in the art that changes could bemade to the disclosed implementations without departing from the broadinventive concept thereof. It is understood, therefore, that thisinvention is not limited to the disclosed implementations, but it isintended to cover modifications within the spirit and scope of thepresent invention as defined by the appended claims.

What is claimed:
 1. A composite data structure recorded onnon-transitory computer readable media for providing a computation modelthat can be applied in multiple execution environments, the datastructure comprising: execution data including an ArtificialIntelligence (AI) inference model specifying predetermined operatingparameters, whereby the execution data will cause an executionenvironment to perform an activity based on the predetermined operatingparameters; an interface definition module including a pointer to aninterface definition associated with the execution data; and a databasepointer including a pointer to a database entry associated with theexecution data.
 2. The composite data structure of claim 1, wherein theinterface definition specifies a finite set of inputs and outputs forthe predetermined processing.
 3. The composite data structure of claim2, wherein the database pointer points to a token stored on adecentralized ledger.
 4. The composite data structure of claim 3,wherein the token is a non-fungible token (NFT).
 5. The composite datastructure of claim 2, wherein the database pointer points to a datafield in a centralized database.
 6. The composite data structure ofclaim 2, wherein the execution data is stored one or moreuni-dimensional and/or multi-dimensional matrices.
 7. The composite datastructure of claim 2, further comprising a multi-dimensional input valuematrix which holds multiple entries that can be mapped to inputvariables within the execution data
 8. The composite data structure ofclaim 2, wherein the activity performed by the execution environment isactivity of an entity in a video game, a metaverse, or an interactiveweb experience.
 9. The composite data structure of claim 8, wherein theinterface definition specifies actions that can be taken by the entityand rules which affect the composite data structure.
 10. A compositedata structure recorded on non-transitory computer readable media forproviding a computation model that can be applied in multiple executionenvironments, the data structure comprising: execution data specifyingpredetermined tasks and an order in which the predetermined tasks areperformed, whereby the execution data will cause the executionenvironment to direct a [model] (trainer) to perform the predeterminedtasks in accordance with the order within the execution environment tothereby modify the AI model of the execution environment; an interfacedefinition module including a pointer to an interface definition of anexecution environment containing associated with the execution data,wherein the execution environment includes a placeholder for anArtificial Intelligence (AI) model that performs an activity; and adatabase pointer including a pointer to a database entry associated withthe execution data.
 11. The composite data structure of claim 10,wherein the pointer to the interface definition specifies an executionenvironment that includes execution data specifying specified tasks andan order in which the specified tasks are performed, whereby theexecution data causes an AI model of a second execution environment toperform the specified tasks in accordance with the order to therebymodify the AI model of the second execution environment.
 12. Thecomposite data structure of claim 10, wherein the interface definitionspecifies a finite set of inputs and outputs for the predeterminedprocessing.
 13. The composite data structure of claim 12, wherein thedatabase pointer points to a token stored on a decentralized ledger. 14.The composite data structure of claim 13, wherein the token is anon-fungible token (NFT).
 15. The composite data structure of claim 12,wherein the database pointer points to a data field in a centralizeddatabase.
 16. The composite data structure of claim 12, wherein theexecution data is stored one or more uni-dimensional and/ormulti-dimensional matrices.